资源 | 一文掌握机器学习所有值得看的视频、课程、工具

AI科技大本营

发布于 2018-04-27 15:47:02

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发布于 2018-04-27 15:47:02

文章被收录于专栏：AI科技大本营的专栏AI科技大本营的专栏

越来越多的开发者和学习者转投人工智能，但很多人鉴于跨行的难度，并不知道如何开始学习人工智能和机器学习。本文是作者Arun Agrahri整理的一些优秀文章，内容包括启发阅读和学习资源以及工具，希望能对你有所帮助。

启发性阅读

还不大了解人工智能行业的小伙伴可以看看以下文章，如果你已经转型AI，可跳过……

https://www.technologyreview.com/s/534871/our-fear-of-artificial-intelligence/

https://www.technologyreview.com/s/603062/ai-winter-isnt-coming/

https://www.nytimes.com/2016/12/14/magazine/the-great-ai-awakening.html?_r=0

https://meetnucleus.com/topics/f182da4c282d59c18a05f705c13b5b64/posts/10df94bf-d932-5f63-98e8-dbc578b7518d?limit=100

https://meetnucleus.com/topics/f182da4c282d59c18a05f705c13b5b64/posts/9a9d957a-9a47-51c3-88d2-5c4fbe55b1fa?limit=100

http://blog.pmarca.com/2014/06/13/this-is-probably-a-good-time-to-say-that-i-dont-believe-robots-will-eat-all-the-jobs/

https://medium.com/@NathanBenaich/6-areas-of-artificial-intelligence-to-watch-closely-673d590aa8aa

https://meetnucleus.com/topics/f182da4c282d59c18a05f705c13b5b64/posts/d2d5232d-08ab-5ac7-9312-af7d5eee7441?limit=100

https://meetnucleus.com/topics/f182da4c282d59c18a05f705c13b5b64/posts/b81ead83-c668-560d-b33c-97321863d4cf?limit=100

https://medium.com/intuitionmachine/the-many-tribes-problem-of-artificial-intelligence-ai-1300faba5b60

https://meetnucleus.com/topics/f182da4c282d59c18a05f705c13b5b64/posts/99c82bcf-122b-5840-9e21-f1e1cd6848dd?limit=100

https://medium.com/the-mission/artificial-intelligence-at-work-the-future-of-workplace-ae0b3a4b1dda

https://medium.com/@NathanBenaich/why-go-long-on-artificial-intelligence-67bb7d0d6ff4

https://blog.semantics3.com/consumer-expectations-in-the-age-of-ai-245e90eab4bc

https://meetnucleus.com/topics/f182da4c282d59c18a05f705c13b5b64/posts/f693d03a-f4bb-56ba-a965-62f3d955f919?limit=100

https://www.oreilly.com/ideas/the-ethics-of-artificial-intelligence

https://meetnucleus.com/topics/f182da4c282d59c18a05f705c13b5b64/posts/de016ff4-4cdd-5a61-92f9-b759f9857b11?limit=100

https://www.wired.com/2016/12/the-ai-takeover-is-coming-lets-embrace-it/

https://hbr.org/2016/10/machine-learning-is-no-longer-just-for-experts

https://hackernoon.com/learning-ai-if-you-suck-at-math-8bdfb4b79037

https://meetnucleus.com/topics/f182da4c282d59c18a05f705c13b5b64/posts/619f69ea-d3dd-5f08-889c-48b6392a7ca8?limit=100

https://medium.com/basic-income/deep-learning-is-going-to-teach-us-all-the-lesson-of-our-lives-jobs-are-for-machines-7c6442e37a49

https://www.wired.com/2016/12/2016-year-deep-learning-took-internet/

AI\ML Tools&Resource

人工智能/机器学习工具和资源（必看）

Best Practices for ML Engineering

http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf

Learn TensorFlow and deep learning, without a Ph.D.

https://meetnucleus.com/p/FQPuryEWqEQW

Fueling the Gold Rush: The Greatest Public Datasets for AI

https://medium.com/startup-grind/fueling-the-ai-gold-rush-7ae438505bc2

Attacking machine learning with adversarial examples

https://openai.com/blog/adversarial-example-research/

Gilt ♥ Open Source / Deep Learning at GILT

https://meetnucleus.com/p/LOQltvwI4iOP

A Beginner’s Guide To Understanding Convolutional Neural Networks

https://meetnucleus.com/p/Y5ZSoQjv7xWS

Unsupervised Feature Learning and Deep Learning Tutorial

http://deeplearning.stanford.edu/tutorial/

Machine Learning is Fun: Modern Face Recognition with Deep Learning

https://meetnucleus.com/p/I4iOPMSaHfor

Image Completion with Deep Learning in TensorFlow

https://meetnucleus.com/p/LpF7jCRgRITC

How to Code and Understand DeepMind’s Neural Stack Machine

https://iamtrask.github.io/2016/02/25/deepminds-neural-stack-machine/

Hacker Noon: Artificial Intelligence

https://hackernoon.com/artificial-intelligence/home

课程（必看）

Artificial Intelligence (AI)

https://meetnucleus.com/p/hWo55dP5Gn6A— Learn the fundamentals of Artificial Intelligence (AI). Design intelligent agents to solve real-world problems including, search, games, machine learning, logic, and constraint satisfaction problems.

Machine Learning

https://meetnucleus.com/p/L2crRcdYTlra—Master the essentials of machine learning and algorithms to help improve learning from data without human intervention.

Learning From Data — Online Course (MOOC)

http://www.work.caltech.edu/telecourse.html — More than 4M views on Youtube and iTunes. Featured on edX. Free Course, Lecture Videos Available.

Machine Learning — University of Washington

https://www.coursera.org/specializations/machine-learning — Master machine learning fundamentals in four hands-on courses.

Neural Networks for Machine Learning

https://www.coursera.org/learn/neural-networks— Learn about artificial neural networks and how they’re being applied to speech and object recognition, image segmentation, modeling language and human motion. Free Course.

Machine Learning — Stanford University

https://www.coursera.org/learn/machine-learning — Free course created by Stanford University, taught by Andrew Ng.

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